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|Headquarters||Menlo Park, California|
(Executive Chairman & Co-Founder)
(VP of Research & Co-Founder)
(Chief Revenue Officer)
(Chief Marketing Officer)
(EVP, Product + Engineering)
(Chief Customer Officer)
(Chief Financial Officer)
|Services||Big Data Analytics Machine Learning|
Number of employees
Ayasdi is a machine intelligence software company that offers a software platform and applications to organizations looking to analyze and build predictive models using big data or highly dimensional data sets. Organizations and governments have deployed Ayasdi's software across a variety of use cases including the development of clinical pathways for hospitals, anti-money laundering, fraud detection, trading strategies, customer segmentation, oil and gas well development, drug development, disease research, information security, anomaly detection and national security applications.
Ayasdi focuses on hypothesis-free, automated analytics at scale. In effect the Ayasdi system consumes the target data set, runs many different unsupervised and supervised machine learning algorithms on the data, automatically finds and ranks best fits, and then applies topological data analysis to find similar groups within the resultant data. It presents the end analysis in the form of a network similarity map, which is useful for an analyst to use to further explore the groupings and correlations that the system has uncovered. This reduces the risk of bias since the system surfaces "what the data says" in an unbiased fashion, rather than relying on analysts or data scientists manually running algorithms in support of pre-existing hypotheses. Ayasdi then generates mathematical models which are deployed in predictive and operational systems and applications.
Organizations using Ayasdi have found Ayasdi's automated, platform-based approach to machine intelligence to be two to five orders of magnitude more efficient than existing approaches to big data analytics, as measured in the amount of time and expense required to complete analysis and build models using large and complex data sets. One widely reported example at a top five global systemically important bank was that to build models required for the annual Comprehensive Capital Analysis and Review (CCAR) process took 1,800 person months with traditional manual big data analytics and machine learning tools, but took 6 person months with Ayasdi. A project at a second global systemically important bank showed Ayasdi reducing the time to build risk models from 3,000 person hours to 10 minutes.
History and funding
Ayasdi was founded in 2008 by Gunnar Carlsson, Gurjeet Singh, and Harlan Sexton after 12 years of research and development at Stanford University. While at Stanford, the founders received $1.25 million in DARPA and IARPA grants for "high-risk, high-payoff research". In 2012 Ayasdi landed a series A round of funding led by Floodgate Capital and Khosla Ventures for $10.25 million. On July 16, 2013, Ayasdi closed $30.6 million in series B funding from Institutional Venture Partners (IVP), GE Ventures, and Citi Ventures. On March 25, 2015, Ayasdi announced a new $55 mullion round of Series C funding, led by Kleiner Perkins Caufield & Byers (KPCB), and joined by existing investors, Institutional Venture Partners (IVP), Khosla Ventures, FLOODGATE, Citi Ventures, and new investors, Centerview Capital Technology and Draper Nexus.
Ayasdi is a machine intelligence platform. It includes dozens of statistical and both supervised and unsupervised machine learning algorithms and can be extended to include whatever algorithms are required for a particular class of analysis. The platform is extensively automated and is in production at scale at many global 100 companies and at governments in the world. It features Topological Data Analysis as a unifying analytical framework, which automatically calculates groupings and similarity across large and highly dimensional data sets, generating network maps with greatly assist analysts in understanding how data clusters and which variables are relevant. When compared with manual approaches to statistical analysis and machine learning, results with Ayasdi will typically be achieved much faster to achieve and more accurate due to the automation and scalability built into the platform. The Ayasdi platform also develops mathematical models, including predictive models, based on the results of the analysis. This allows Ayasdi to deployed as an operational system, or as a part of operational systems, and not just for analysis.
Ayasdi also develops machine intelligence applications. One example is Ayasdi Care, which is a suite of cloud-based applications for healthcare providers that is focused on managing and improving patient outcomes, revenue and population health. For example, Ayasdi clinical variation, one of the applications in Ayasdi Care, automatically discovers the ideal care paths for medical procedures based on analyzing historical patient data, billing records and insurance claims.
Users and industries
Ayasdi customers include many large enterprises, medical research institutions and governments across industries including health care, financial services, oil and gas, security, life sciences, and the public sector.
Ayasdi competes with a variety of big data analytics and machine learning vendors, most commonly SAS, IBM and Palantir from the commercial perspective as well as open source projects like R and Spark.
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